Evidence map›Paper›PMID 38224784›Full record

ArticleThe Journal of allergy and clinical immunology2024

Proceedings from the inaugural Artificial Intelligence in Primary Immune Deficiencies (AIPID) conference.

Jacques G Rivière, Pere Soler Palacín, Manish J Butte

Open access · bronzeAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in The Journal of allergy and clinical immunology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed, 1 pooled it
4.8field-weighted citation impact, top 4% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

13 citing papers in PubMed, 1 synthesis or guideline pooled it, 20 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors at 2 institutions in 2 countries.

Jacques G RivièreInfection and Immunity in Pediatric Patients Research Group, Vall d'Hebron Barcelona Hospital Campus, Barcelona, Spain; Pediatric Infectious Diseases and Immunodeficiencies Unit, Hospital Infantil i de la Dona, Vall d'Hebron Barcelona Hospital Campus, Barcelona, Spain; Jeffrey Modell Diagnostic and Research Center for Primary Immunodeficiencies, Barcelona, Spain; Universitat Autònoma de Barcelona, Barcelona, Spain.
Pere Soler PalacínInfection and Immunity in Pediatric Patients Research Group, Vall d'Hebron Barcelona Hospital Campus, Barcelona, Spain; Pediatric Infectious Diseases and Immunodeficiencies Unit, Hospital Infantil i de la Dona, Vall d'Hebron Barcelona Hospital Campus, Barcelona, Spain; Jeffrey Modell Diagnostic and Research Center for Primary Immunodeficiencies, Barcelona, Spain; Universitat Autònoma de Barcelona, Barcelona, Spain.
Manish J ButteDivision of Immunology, Allergy, and Rheumatology, Department of Pediatrics, University of California Los Angeles, Los Angeles, Calif; Department of Microbiology, Immunology, and Molecular Genetics, University of California Los Angeles, Los Angeles, Calif; Department of Human Genetics, University of California Los Angeles, Los Angeles, Calif. Electronic address: mbutte@mednet.ucla.edu.
Universitat Autònoma de Barcelona · ESUniversity of California, Los Angeles · US

Funding

Collaborative multi-site project to speed the identification and management of rare genetic immune diseasesR01AI153827 · NIAID · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI BUTTE, MANISH J, PASANIUC, BOGDAN · 2021 to 2025
$3.9M
NIAID NIH HHS R01 AI153827
6 · The paper itself

Abstract

Here, we summarize the proceedings of the inaugural Artificial Intelligence in Primary Immune Deficiencies conference, during which experts and advocates gathered to advance research into the applications of artificial intelligence (AI), machine learning, and other computational tools in the diagnosis and management of inborn errors of immunity (IEIs). The conference focused on the key themes of expediting IEI diagnoses, challenges in data collection, roles of natural language processing and large language models in interpreting electronic health records, and ethical considerations in implementation. Innovative AI-based tools trained on electronic health records and claims databases have discovered new patterns of warning signs for IEIs, facilitating faster diagnoses and enhancing patient outcomes. Challenges in training AIs persist on account of data limitations, especially in cases of rare diseases, overlapping phenotypes, and biases inherent in current data sets. Furthermore, experts highlighted the significance of ethical considerations, data protection, and the necessity for open science principles. The conference delved into regulatory frameworks, equity in access, and the imperative for collaborative efforts to overcome these obstacles and harness the transformative potential of AI. Concerted efforts to successfully integrate AI into daily clinical immunology practice are still needed.

Indexed as

Artificial IntelligencePrimary Immunodeficiency DiseasesData CollectionHumansMachine LearningNatural Language ProcessingArtificial intelligencediagnosiselectronic health recordsethicsinborn errors of immunitylarge language modelsmachine learningnatural language processing

Identifiers

PMID38224784
PMCPMC11402388
OpenAlexW4390842187

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.